1. Field of the Invention
The invention relates to the field of input imaging devices, and more particularly relates to generating a color characterization model for an input imaging device.
2. Description of the Related Art
Color characterization typically refers to a process that results in a model or a profile, which models the color characteristics and properties of an input imaging device. Models and profiles are commonly used to predict the color characteristics of the input imaging device.
Color characterization of an input imaging device typically involves the use of a target with multiple color patches thereon. For example, the target may be a GretagMacbeth ColorChecker DC color chart, which typically has 240 differently-colored patches thereon. To characterize the input imaging device, two independent measurements can be made. The first measurement is typically made with a highly accurate device such as a spectroradiometer, so as to provide a standard against which the second measurement is compared. The second measurement is typically made with the input imaging device. The measurements are then compared, and a mapping is created so as to provide the color characterization for the input imaging device.
However, using all available or pre-selected patches for color characterization of the input imaging device may result in lower accuracy for the color characterization. In this regard, characterization algorithms are generally based on the assumption of a unique relationship between measurements of the target and corresponding digital values produced by the input imaging device. In other words, it is typically assumed that every color of the target when captured by the input imaging device is represented by a unique combination of device values. Also, if digital values are different, colors of the target are assumed to be different. Thus, the capture of a color target with a unique set of color patches should result in a unique set of device values. In general, if this uniqueness assumption is not fulfilled, the characterization algorithms may produce suboptimal results.
However, there are two situations when the uniqueness assumption may fail. The first situation deals with uneven lighting conditions, where lighting is positioned in such way that different parts of the target are illuminated differently. For example, light intensity may change across a target, or spectral power distribution of the light falling on the target may vary across the target. In such cases, patches of the same reflectance located at different parts of the target may produce different device values when captured by the input imaging device. Further, uneven lighting may result in patches of different colors having the same device values.
A second situation in which the uniqueness assumption may fail is what may be referred to as the generic assumption on lighting conditions and input imaging devices. Targets are usually designed for a generic device and for generic lighting conditions. Thus, targets may ignore the specific behaviors and characteristics of a particular device and/or specific lighting conditions. In this regard, a certain input imaging device may distinguish a pair of patches under certain illumination, while another input imaging device may render the pair of patches into the same device values. In addition, the use of a light source that contains a significant chromatic component may result in rendering some patches as indistinguishable by a specific input imaging device. For example, if a color target is lit by blue light, an input imaging device may not discriminate some patches in the red area. This may lead to less accuracy when characterizing the input imaging device.
Thus, there is a need for systems and methods for improved accuracy when generating a color characterization model for an input imaging device.